CNN-derived brain age gaps of different neurological and cardiovascular diseases in the UK Biobank and identifying affected brain regions

Abstract

The difference between chronological age and predicted biological brain age, the so-called "brain age gap", is a promising biomarker for assessment of overall brain health. It has also been suggested as a biomarker for early detection of neurological and cardiovascular conditions. The aim of this work is to identify group-level variability in the brain age gap between healthy subjects and patients with neurological and cardiovascular diseases. Therefore, a deep convolutional neural network was trained on UK Biobank T1-weighted-MRI datasets of healthy subjects (n=6860) to predict brain age. After training, the model was used to determine the brain age gap for healthy hold-out test subjects (n=344), and subjects with neurological (n=2327) or cardiovascular (n=6467) diseases. Next, saliency maps were analyzed to identify brain regions used by the model to render decisions. Linear bias correction was implemented to correct for the bias of age predictions made by the model. The trained model after bias correction achieved an average brain age gap of 0.05 years for the healthy test cohort while the neurological disease test cohort had an average brain age gap of 0.7 years, and the cardiovascular disease test cohort had an average brain age gap of 0.25 years. The average saliency maps appear similar for the three test group, suggesting that the model mostly uses brain areas associated with general brain aging patterns. This works results indicate potential in the brain age gap for differentiation of neurologic and cardiac patients from healthy aging patterns supporting its use as a novel biomarker.